NARCH RNAseq analysis
Miles Smith
08 October, 2020
Disease classification | Number | Percent |
control | 77 | 41.0 |
RA | 55 | 29.3 |
SLE | 56 | 29.8 |
Differential Gene Expression
Comparison | # Up | % Up | # Down | % Down | Mean count | # Low | % Low |
RA vs control | 1690 | 4.35 | 1886 | 4.85 | 2 | 18285 | 47.01 |
SLE vs control | 2248 | 5.78 | 2377 | 6.11 | 2 | 17538 | 45.09 |
25 genes with largest negative log-fold change in experimental group
## RA_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
HSPA1B
7.5
0.42
6.3e-75
6.5e-71
HSPA6
7
0.50
1.0e-55
5.2e-52
HSPA1A
6.8
0.39
1.8e-79
3.6e-75
AL671762.1
6.2
0.39
4.1e-61
2.8e-57
GJB2
5.2
1.20
6.5e-06
1.8e-04
CCL22
5
0.77
6.8e-13
1.3e-10
MIR210HG
4.5
0.88
6.1e-15
2.0e-12
HSPB1
3.6
0.29
3.5e-40
1.2e-36
LINC00656
3.4
1.00
7.5e-04
8.2e-03
SNORD14E
3.1
0.51
3.0e-11
4.2e-09
THBS1
3.1
0.38
5.6e-19
3.4e-16
AC016168.2
3
0.95
1.8e-04
2.7e-03
CREM
3
0.25
1.5e-32
3.9e-29
ERRFI1
3
0.46
9.6e-14
2.3e-11
HSPH1
2.9
0.27
1.1e-30
2.3e-27
AC105105.1
2.8
0.72
1.7e-06
5.6e-05
DNAJB1
2.8
0.20
2.6e-46
1.1e-42
LIF
2.8
0.57
3.0e-09
2.3e-07
SLC6A8
2.7
0.44
2.7e-12
4.8e-10
PHLDB1
2.5
0.55
5.2e-08
2.8e-06
ADM
2.4
0.38
3.1e-12
5.2e-10
DNAH17
2.4
0.60
1.3e-07
6.1e-06
YPEL4
2.4
0.43
7.1e-11
8.6e-09
ATF3
2.3
0.24
2.4e-23
2.5e-20
NGFR
2.3
0.69
8.0e-06
2.1e-04
*Wald test p-values
†Benjamini–Hochberg adjusted value
SLE_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
HSPA1B
7.1
0.41
7.1e-67
7.6e-63
HSPA1A
6.6
0.39
9.7e-76
2.1e-71
TFPI2
6.1
3.00
4.9e-08
2.4e-06
CCL22
4.8
0.78
2.0e-12
3.5e-10
MIR210HG
4.7
0.80
4.7e-16
1.7e-13
AC011498.4
4.6
0.94
5.0e-11
6.3e-09
AP000941.1
4.5
1.40
1.5e-04
1.8e-03
SEMA6B
4.3
0.55
3.7e-17
1.7e-14
CBARP
4.2
1.00
5.9e-06
1.3e-04
LINC02029
4
1.20
2.1e-04
2.3e-03
HCG20
3.6
0.96
1.0e-04
1.3e-03
AC112496.1
3.5
0.85
1.3e-04
1.5e-03
HILPDA
3.5
0.75
1.9e-13
4.2e-11
SERPINB2
3.3
0.65
2.0e-09
1.6e-07
THBS1
3.3
0.41
9.1e-21
6.5e-18
OLR1
3.2
0.62
6.1e-10
5.7e-08
ABHD17AP6
3.1
1.10
2.4e-03
1.6e-02
CXCL5
3.1
1.20
3.1e-07
1.1e-05
HSPH1
3.1
0.26
7.1e-35
2.2e-31
NRG1
3
0.63
5.9e-10
5.5e-08
SNORD14E
3
0.51
7.9e-11
9.3e-09
HSPB1
2.9
0.29
4.6e-26
7.6e-23
TRPV3
2.9
0.66
2.3e-08
1.3e-06
DNAJB1
2.8
0.20
9.4e-46
4.0e-42
GRIN3B
2.8
0.73
7.2e-07
2.3e-05
*Wald test p-values
†Benjamini–Hochberg adjusted value
25 genes with largest positive log-fold change in experimental group
## RA_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
ACVRL1
4.9
0.86
5.2e-12
8.2e-10
TRBV12-4
4.3
0.91
6.8e-06
1.8e-04
IGLV9-49
4.1
0.79
8.7e-10
7.8e-08
HBB
3.7
0.88
7.0e-07
2.7e-05
ARLNC1
3.5
1.30
2.2e-03
1.8e-02
IGKV1-17
3.5
0.51
3.0e-14
8.3e-12
IGKV2D-28
3.5
0.49
3.9e-16
1.5e-13
IGLV2-5
3.5
0.83
3.6e-04
4.6e-03
TMEM198
3.5
0.91
2.5e-07
1.1e-05
TNFRSF17
3.5
0.52
3.3e-13
7.3e-11
IGLV3-19
3.4
0.50
9.0e-14
2.2e-11
AL162724.1
3.3
0.82
5.7e-06
1.6e-04
IGHV2-70D
3.3
0.80
7.9e-06
2.1e-04
TRBV5-1
3.2
0.57
5.6e-11
7.0e-09
TRBV7-6
3.2
0.83
6.3e-04
7.1e-03
FAM171A1
3.1
0.64
1.3e-07
6.1e-06
FAM218A
3.1
0.93
5.0e-04
6.0e-03
LINC00402
3.1
0.67
3.4e-07
1.4e-05
LINC02033
3
0.64
4.7e-07
1.9e-05
AL392172.2
2.9
1.30
3.0e-03
2.3e-02
AP001107.9
2.9
0.60
3.5e-08
2.0e-06
IGHV1-69D
2.9
0.53
1.2e-10
1.4e-08
IGLC6
2.9
0.99
1.6e-03
1.5e-02
RPL23AP81
2.9
0.73
3.1e-05
6.5e-04
FAM238B
2.8
0.94
2.4e-04
3.3e-03
HBA1
2.8
0.82
2.5e-05
5.4e-04
IGHV4-55
2.8
0.78
1.2e-05
3.0e-04
*Wald test p-values
†Benjamini–Hochberg adjusted value
SLE_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
IFI27
6.1
0.69
2.3e-21
1.8e-18
ELOVL6
5
0.93
3.9e-13
8.0e-11
IGLVI-70
4.7
1.00
2.9e-06
7.3e-05
CERS3
3.6
0.84
4.6e-06
1.1e-04
IGHGP
3.6
0.61
1.0e-10
1.2e-08
OTOF
3.3
0.59
6.3e-11
7.6e-09
Z99756.1
3.2
0.91
2.7e-06
6.8e-05
LINC00487
3.1
0.76
6.8e-08
3.2e-06
RTEL1P1
3.1
0.80
1.8e-06
4.8e-05
STOX1
3
0.85
4.0e-05
6.1e-04
IFI44L
2.9
0.30
1.3e-23
1.6e-20
IGLV7-46
2.8
0.56
3.2e-09
2.3e-07
AL512353.1
2.7
0.91
1.9e-05
3.4e-04
CYP2J2
2.7
0.94
3.0e-03
1.9e-02
IGHG1
2.6
0.31
1.1e-19
7.4e-17
LY6E-DT
2.6
0.72
4.5e-06
1.0e-04
USP18
2.6
0.41
8.0e-12
1.2e-09
C11orf45
2.5
0.88
4.7e-04
4.5e-03
IGHV1-69
2.5
0.57
9.2e-08
4.1e-06
KCNG1
2.5
0.81
4.7e-05
7.1e-04
LINC02033
2.5
0.65
1.2e-05
2.3e-04
NRIR
2.5
0.51
1.4e-08
8.4e-07
RUFY4
2.5
0.49
1.9e-10
2.0e-08
SIGLEC1
2.5
0.48
6.0e-09
4.0e-07
AC009686.2
2.4
0.74
5.2e-04
4.8e-03
ACTA2-AS1
2.4
0.64
2.6e-05
4.4e-04
TLDC2
2.4
0.77
2.0e-06
5.4e-05
*Wald test p-values
†Benjamini–Hochberg adjusted value
Expression of top class DE genes by class
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Expression of all top DE genes
Sorted by disease classification
Sorted by cell type
Sorted by cluster
Sorted by hierarchical clustering
Volcano plot of gene expression
## Warning: Removed 18286 rows containing missing values (geom_point).
## Warning: Removed 17539 rows containing missing values (geom_point).
Expression of selected interferon-stimulated genes
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
Expression of individual genes in the M1.2, M3.4, and M5.12 gene modules
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
Sample Clustering
The eigenvales for the annotated Banchereau modules were used to calculate an adjacentcy matrix, which was in turn used to calculate the gap statistic to determine the optimal k-clusters.
## `summarise()` regrouping output by 'disease_class' (override with `.groups` argument)
Clustering and variation of samples in reduced dimensional space
PCA
PCA by disease classification
PCA by cell type
PCA by ethnicity
PCA by K-means cluster
UMAP
UMAP by disease classification
UMAP by cell type
UMAP by ethnicity
UMAP by K-means cluster
Module scores
SLE Interferon module scores
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
SLE Inflammatory module scores
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
Low-density granulocyte module scores
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
SLE Modules with an annotated associated pathway
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
All SLE modules
## ℹ No targets to load in loadd().Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
Expression of the annotated Banchereau modules
By disease classification
By cell_type
By cluster
WGCNA
Expression similarity dendrogram
Expression of identified modules
By disease classification
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
By cell_type
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
By cluster
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
WGCNA eigenvalues
Sorted by disease classification
Sorted by cell_type
Sorted by cluster
Sorted by hierarchical clustering
Gene ontology GSEA of WGCNA modules
Use of WGCNA modules in classifying clusters.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
Note: Subjects with LP were excluded due to the small population size.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8
## 1 5 0 0 0 0 5 0 0
## 2 0 4 0 1 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 6 1 9 0 0 0 0
## 5 0 0 0 0 0 0 2 1
## 6 1 0 0 0 0 3 1 0
## 7 0 0 0 0 0 0 1 1
## 8 0 0 0 0 0 0 4 1
##
## Overall Statistics
##
## Accuracy : 0.5
## 95% CI : (0.349, 0.651)
## No Information Rate : 0.2174
## P-Value [Acc > NIR] : 2.259e-05
##
## Kappa : 0.4039
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
## Sensitivity 0.8333 0.40000 0.00000 0.9000 NA 0.37500 0.12500 0.33333
## Specificity 0.8750 0.97222 1.00000 0.8056 0.93478 0.94737 0.97368 0.90698
## Pos Pred Value 0.5000 0.80000 NaN 0.5625 NA 0.60000 0.50000 0.20000
## Neg Pred Value 0.9722 0.85366 0.97826 0.9667 NA 0.87805 0.84091 0.95122
## Prevalence 0.1304 0.21739 0.02174 0.2174 0.00000 0.17391 0.17391 0.06522
## Detection Rate 0.1087 0.08696 0.00000 0.1957 0.00000 0.06522 0.02174 0.02174
## Detection Prevalence 0.2174 0.10870 0.00000 0.3478 0.06522 0.10870 0.04348 0.10870
## Balanced Accuracy 0.8542 0.68611 0.50000 0.8528 NA 0.66118 0.54934 0.62016The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 22)
##
## 1 2 3 4 5 6 7 8
## MEblue 14.3693 7.82545 -0.97829 8.095 0.88383 6.71602 6.422 4.4448
## MEturquoise 8.5627 5.71845 -1.88396 14.235 0.06745 1.63373 1.897 7.3597
## MEpink 11.5824 3.81770 -0.26493 6.681 -0.07100 3.47044 2.054 0.8572
## MEgrey60 11.5603 6.03835 -0.62524 6.218 1.83138 5.78396 5.676 1.3140
## MEmidnightblue 11.2442 5.38447 0.11599 7.883 -1.72204 0.90253 8.619 6.1353
## MEbrown 9.9589 6.41273 -3.25380 10.646 0.22458 4.94336 6.648 4.9923
## MEpurple 6.7482 -2.21446 -2.26207 10.334 1.53567 1.47778 5.543 1.2884
## MEcyan 5.9730 1.99198 -2.14758 10.267 0.22573 0.08171 7.071 4.2144
## MEblack 6.5315 4.94709 -2.44302 10.115 3.34137 0.32909 1.586 -1.8805
## MEyellow 7.5887 5.58255 1.05781 9.419 0.59289 1.83879 2.558 5.0972
## MEtan 7.1845 4.15669 0.03735 7.270 0.01082 0.50122 4.931 8.3790
## MElightyellow 8.1430 2.42064 1.70996 5.354 1.69733 2.54109 6.027 3.5652
## MEred 7.7584 3.77223 -1.78087 6.651 1.01509 0.54880 6.746 5.6750
## MEdarkred 7.4979 1.85037 -2.61042 6.125 0.31279 1.61495 5.423 1.1253
## MEgreenyellow 4.1659 -0.03084 -2.64787 6.788 -0.74295 2.01727 2.987 2.8514
## MEgreen 6.1628 5.94511 -2.27799 4.344 -0.14282 -1.52697 1.140 1.0719
## MElightgreen 6.0871 -3.06843 -0.28870 2.069 -0.77411 -2.03344 2.961 1.3398
## MEdarkgreen -0.2039 1.89077 -2.10168 2.749 -0.32412 0.55297 5.400 -3.7847
## MEsalmon 5.3525 3.63849 1.01297 3.311 0.08243 -1.30895 1.571 0.4298
## MElightcyan 2.5020 2.61766 0.16440 5.170 1.37282 -0.82609 2.747 -0.2886Use of WGCNA modules in labeling disease class.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control RA SLE
## control 16 2 1
## RA 2 5 8
## SLE 1 6 5
##
## Overall Statistics
##
## Accuracy : 0.5652
## 95% CI : (0.4111, 0.7107)
## No Information Rate : 0.413
## P-Value [Acc > NIR] : 0.02666
##
## Kappa : 0.3391
##
## Mcnemar's Test P-Value : 0.96269
##
## Statistics by Class:
##
## Class: control Class: RA Class: SLE
## Sensitivity 0.8421 0.3846 0.3571
## Specificity 0.8889 0.6970 0.7812
## Pos Pred Value 0.8421 0.3333 0.4167
## Neg Pred Value 0.8889 0.7419 0.7353
## Prevalence 0.4130 0.2826 0.3043
## Detection Rate 0.3478 0.1087 0.1087
## Detection Prevalence 0.4130 0.3261 0.2609
## Balanced Accuracy 0.8655 0.5408 0.5692The relative importance of each eigengene in classification:
## parRF variable importance
##
## only 20 most important variables shown (out of 22)
##
## Overall
## MElightgreen 25.735
## MEroyalblue 5.533
## MElightcyan 5.029
## MEred 4.408
## MEdarkgreen 3.831
## MElightyellow 3.762
## MEcyan 3.433
## MEgrey60 3.403
## MEblack 3.382
## MEtan 3.289
## MEsalmon 3.213
## MEgreenyellow 3.166
## MEblue 2.976
## MEgreen 2.948
## MEdarkred 2.877
## MEmidnightblue 2.718
## MEmagenta 2.417
## MEpurple 2.357
## MEyellow 2.271
## MEbrown 2.206Use of module modules in classifying clusters.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8
## 1 6 0 0 0 0 3 0 0
## 2 0 5 0 4 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 3 0 7 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 4 0 0
## 7 0 0 0 0 1 0 4 2
## 8 0 0 0 0 0 0 3 2
##
## Overall Statistics
##
## Accuracy : 0.6222
## 95% CI : (0.4654, 0.7623)
## No Information Rate : 0.2444
## P-Value [Acc > NIR] : 8.541e-08
##
## Kappa : 0.5436
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
## Sensitivity 0.8571 0.6250 NA 0.6364 0.00000 0.57143 0.57143 0.50000
## Specificity 0.9211 0.8919 1 0.9118 1.00000 0.97368 0.92105 0.92683
## Pos Pred Value 0.6667 0.5556 NA 0.7000 NaN 0.80000 0.57143 0.40000
## Neg Pred Value 0.9722 0.9167 NA 0.8857 0.97778 0.92500 0.92105 0.95000
## Prevalence 0.1556 0.1778 0 0.2444 0.02222 0.15556 0.15556 0.08889
## Detection Rate 0.1333 0.1111 0 0.1556 0.00000 0.08889 0.08889 0.04444
## Detection Prevalence 0.2000 0.2000 0 0.2222 0.00000 0.11111 0.15556 0.11111
## Balanced Accuracy 0.8891 0.7584 NA 0.7741 0.50000 0.77256 0.74624 0.71341The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 260)
##
## 1 2 3 4 5 6 7 8
## M9.50 1.3644 1.3989 2.309e-01 0.0000 7.9646 -1.4095 -2.2869 16.6677
## M5.15 14.9597 2.6251 3.377e+00 8.3911 6.1483 3.4568 9.8552 8.5809
## M8.22 1.3281 12.3497 -2.536e+00 1.2379 -1.7922 1.0010 2.2928 1.7264
## M8.54 11.0772 0.1854 6.404e-01 1.0010 0.0000 -4.1093 0.1562 0.0000
## M4.1 9.4637 9.7319 -3.584e+00 10.9203 5.1621 7.1675 7.4282 8.2874
## M8.41 8.2247 10.2774 -5.887e+00 10.3112 3.7191 6.8320 9.5280 8.4319
## M4.15 9.0549 8.7563 -1.581e+00 9.8181 4.5581 4.7102 7.7209 7.9695
## M6.13 2.5102 1.1739 2.577e+00 2.7801 1.0910 -1.9585 -1.0567 8.4132
## M8.58 8.0103 4.3608 2.869e+00 4.8290 4.0826 8.2278 3.0940 5.4960
## M5.14 3.5232 -2.8498 -1.143e+00 7.8671 1.9599 2.2626 2.2390 3.6788
## M7.17 2.7396 -4.3366 4.230e-17 7.2724 1.6691 3.2706 2.7833 2.9750
## M9.30 7.1216 -0.2774 2.898e+00 1.0010 4.0843 3.9310 5.5550 5.9115
## M6.16 0.8473 -3.9059 -1.344e+00 7.0236 0.3180 0.5205 1.3714 1.7241
## M8.71 6.9733 1.6374 1.327e+00 1.3910 4.6504 4.5508 6.8717 6.2373
## M9.24 6.2781 -3.8498 1.214e+00 5.8269 0.1690 -0.8202 1.0694 5.3447
## M9.15 1.6271 -1.9084 -1.001e+00 5.9538 1.4171 1.1688 1.7262 1.5498
## M8.56 5.6236 0.4820 -8.464e-01 1.3773 3.3963 4.7765 3.0064 4.6920
## M7.29 1.0010 0.8445 3.848e+00 2.3988 1.9396 0.9768 5.3479 4.3251
## M4.3 1.0010 5.2245 1.178e+00 0.1366 0.0000 1.0010 -0.7752 0.4845
## M6.6 3.5675 3.4052 -9.351e-01 4.8004 0.4658 3.4305 5.1912 4.1304Use of module modules in labeling disease class.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control RA SLE
## control 16 3 1
## RA 2 6 7
## SLE 1 4 6
##
## Overall Statistics
##
## Accuracy : 0.6087
## 95% CI : (0.4537, 0.7491)
## No Information Rate : 0.413
## P-Value [Acc > NIR] : 0.005811
##
## Kappa : 0.403
##
## Mcnemar's Test P-Value : 0.796853
##
## Statistics by Class:
##
## Class: control Class: RA Class: SLE
## Sensitivity 0.8421 0.4615 0.4286
## Specificity 0.8519 0.7273 0.8438
## Pos Pred Value 0.8000 0.4000 0.5455
## Neg Pred Value 0.8846 0.7742 0.7714
## Prevalence 0.4130 0.2826 0.3043
## Detection Rate 0.3478 0.1304 0.1304
## Detection Prevalence 0.4348 0.3261 0.2391
## Balanced Accuracy 0.8470 0.5944 0.6362The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 260)
##
## control RA SLE
## M2.3 13.93671 7.46191 5.7414
## M3.4 5.22186 -2.15418 8.1796
## M8.77 3.56442 7.05577 0.1395
## M8.98 6.93188 4.85291 0.9418
## M8.10 5.70636 3.49268 1.3072
## M8.93 4.75082 0.10196 3.7531
## M5.12 3.20252 1.13290 4.7210
## M1.2 4.32166 0.27341 4.3726
## M7.1 0.13589 4.31162 -0.8437
## M8.87 0.88386 4.10724 0.7403
## M4.8 3.80243 0.12635 2.0789
## M4.11 3.75735 0.02174 1.9477
## M9.23 0.93460 -1.00056 3.7241
## M8.86 0.06736 3.57070 -1.2428
## M8.6 1.82644 -1.85412 3.4964
## M7.16 1.85245 3.34325 0.6618
## M8.110 3.30882 0.80783 -0.8009
## M6.3 3.28167 0.56511 2.3193
## M9.10 1.37740 3.23515 -1.4084
## M8.44 3.22465 -0.82291 1.4468## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.0 (2020-04-24)
## os Ubuntu 18.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-10-08
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.0.0)
## acepack 1.4.1 2016-10-29 [1] RSPM (R 4.0.0)
## annotate 1.66.0 2020-04-27 [1] Bioconductor
## AnnotationDbi 1.50.3 2020-07-25 [1] Bioconductor
## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.0)
## backports 1.1.7 2020-05-13 [1] RSPM (R 4.0.0)
## base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.0.0)
## base64url 1.4 2018-05-14 [1] RSPM (R 4.0.0)
## bayesplot 1.7.2 2020-05-28 [1] RSPM (R 4.0.0)
## bibtex 0.4.2.2 2020-01-02 [1] RSPM (R 4.0.0)
## Biobase * 2.48.0 2020-04-27 [1] Bioconductor
## BiocGenerics * 0.34.0 2020-04-27 [1] Bioconductor
## BiocManager * 1.30.10 2019-11-16 [1] CRAN (R 4.0.0)
## BiocParallel * 1.22.0 2020-04-27 [1] Bioconductor
## bit 1.1-15.2 2020-02-10 [1] RSPM (R 4.0.0)
## bit64 0.9-7 2017-05-08 [1] RSPM (R 4.0.0)
## bitops 1.0-6 2013-08-17 [1] RSPM (R 4.0.0)
## blob 1.2.1 2020-01-20 [1] RSPM (R 4.0.0)
## boot 1.3-25 2020-04-26 [2] RSPM (R 4.0.0)
## broom * 0.5.6 2020-04-20 [1] RSPM (R 4.0.0)
## callr 3.4.3 2020-03-28 [1] RSPM (R 4.0.0)
## car 3.0-8 2020-05-21 [1] RSPM (R 4.0.0)
## carData 3.0-4 2020-05-22 [1] RSPM (R 4.0.0)
## caret * 6.0-86 2020-03-20 [1] RSPM (R 4.0.0)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.0.0)
## checkmate 2.0.0 2020-02-06 [1] RSPM (R 4.0.0)
## class 7.3-17 2020-04-26 [2] RSPM (R 4.0.0)
## cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.0)
## cluster * 2.1.0 2019-06-19 [1] RSPM (R 4.0.0)
## clusterProfiler * 3.16.1 2020-08-18 [1] Bioconductor
## codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.0)
## colorspace 1.4-1 2019-03-18 [1] RSPM (R 4.0.0)
## colourpicker 1.0 2017-09-27 [1] RSPM (R 4.0.0)
## corrplot * 0.84 2017-10-16 [1] RSPM (R 4.0.0)
## cowplot * 1.0.0 2019-07-11 [1] RSPM (R 4.0.0)
## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0)
## crosstalk 1.1.0.1 2020-03-13 [1] RSPM (R 4.0.0)
## curl 4.3 2019-12-02 [1] RSPM (R 4.0.0)
## data.table 1.12.8 2019-12-09 [1] RSPM (R 4.0.0)
## DBI 1.1.0 2019-12-15 [1] RSPM (R 4.0.0)
## dbplyr 1.4.4 2020-05-27 [1] RSPM (R 4.0.0)
## DelayedArray * 0.14.1 2020-07-14 [1] Bioconductor
## DESeq2 * 1.28.1 2020-05-12 [1] Bioconductor
## dials * 0.0.6 2020-04-03 [1] RSPM (R 4.0.0)
## DiceDesign 1.8-1 2019-07-31 [1] RSPM (R 4.0.0)
## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0)
## DO.db 2.9 2020-09-18 [1] Bioconductor
## doParallel 1.0.15 2019-08-02 [1] RSPM (R 4.0.0)
## DOSE 3.14.0 2020-04-27 [1] Bioconductor
## downloader 0.4 2015-07-09 [1] RSPM (R 4.0.0)
## dplyr * 1.0.0 2020-05-29 [1] RSPM (R 4.0.0)
## drake * 7.12.2 2020-06-02 [1] RSPM (R 4.0.0)
## DT 0.13 2020-03-23 [1] RSPM (R 4.0.0)
## dygraphs 1.1.1.6 2018-07-11 [1] RSPM (R 4.0.0)
## dynamicTreeCut * 1.63-1 2016-03-11 [1] RSPM (R 4.0.0)
## e1071 1.7-3 2019-11-26 [1] RSPM (R 4.0.0)
## edgeR 3.30.3 2020-06-02 [1] Bioconductor
## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.0)
## enrichplot 1.8.1 2020-04-29 [1] Bioconductor
## europepmc 0.4 2020-05-31 [1] RSPM (R 4.0.0)
## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.0)
## factoextra * 1.0.7 2020-04-01 [1] RSPM (R 4.0.0)
## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0)
## farver 2.0.3 2020-01-16 [1] RSPM (R 4.0.0)
## fastcluster * 1.1.25 2018-06-07 [1] RSPM (R 4.0.0)
## fastmap 1.0.1 2019-10-08 [1] RSPM (R 4.0.0)
## fastmatch 1.1-0 2017-01-28 [1] RSPM (R 4.0.0)
## fgsea 1.14.0 2020-04-27 [1] Bioconductor
## filelock 1.0.2 2018-10-05 [1] RSPM (R 4.0.0)
## flextable * 0.5.10 2020-05-15 [1] RSPM (R 4.0.0)
## forcats * 0.5.0 2020-03-01 [1] RSPM (R 4.0.0)
## foreach 1.5.0 2020-03-30 [1] RSPM (R 4.0.0)
## foreign 0.8-80 2020-05-24 [2] RSPM (R 4.0.0)
## formattable * 0.2.0.1 2016-08-05 [1] RSPM (R 4.0.0)
## Formula 1.2-3 2018-05-03 [1] RSPM (R 4.0.0)
## fs 1.4.1 2020-04-04 [1] RSPM (R 4.0.0)
## furrr * 0.1.0 2018-05-16 [1] RSPM (R 4.0.0)
## future * 1.17.0 2020-04-18 [1] RSPM (R 4.0.0)
## gdtools 0.2.2 2020-04-03 [1] RSPM (R 4.0.0)
## genefilter * 1.70.0 2020-04-27 [1] Bioconductor
## geneplotter 1.66.0 2020-04-27 [1] Bioconductor
## generics 0.0.2 2018-11-29 [1] RSPM (R 4.0.0)
## GenomeInfoDb * 1.24.2 2020-06-15 [1] Bioconductor
## GenomeInfoDbData 1.2.3 2020-09-18 [1] Bioconductor
## GenomicRanges * 1.40.0 2020-04-27 [1] Bioconductor
## ggforce * 0.3.1 2019-08-20 [1] RSPM (R 4.0.0)
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## ggplotify * 0.0.5 2020-03-12 [1] RSPM (R 4.0.0)
## ggpubr * 0.3.0 2020-05-04 [1] RSPM (R 4.0.0)
## ggradar * 0.2 2020-09-18 [1] Github (ricardo-bion/ggradar@63e5cef)
## ggraph 2.0.3 2020-05-20 [1] RSPM (R 4.0.0)
## ggrepel * 0.8.2 2020-03-08 [1] RSPM (R 4.0.0)
## ggridges 0.5.2 2020-01-12 [1] RSPM (R 4.0.0)
## ggsignif 0.6.0 2019-08-08 [1] RSPM (R 4.0.0)
## ggtext * 0.1.0 2020-06-04 [1] RSPM (R 4.0.0)
## globals 0.12.5 2019-12-07 [1] RSPM (R 4.0.0)
## glue 1.4.1 2020-05-13 [1] RSPM (R 4.0.0)
## GO.db 3.11.4 2020-09-18 [1] Bioconductor
## GOSemSim 2.14.2 2020-09-04 [1] Bioconductor
## gower 0.2.1 2019-05-14 [1] RSPM (R 4.0.0)
## GPfit 1.0-8 2019-02-08 [1] RSPM (R 4.0.0)
## graphlayouts 0.7.0 2020-04-25 [1] RSPM (R 4.0.0)
## gridBase 0.4-7 2014-02-24 [1] RSPM (R 4.0.0)
## gridExtra 2.3 2017-09-09 [1] RSPM (R 4.0.0)
## gridGraphics 0.5-0 2020-02-25 [1] RSPM (R 4.0.0)
## gridtext 0.1.1 2020-02-24 [1] RSPM (R 4.0.0)
## gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.0)
## gtools * 3.8.2 2020-03-31 [1] RSPM (R 4.0.0)
## haven 2.3.1 2020-06-01 [1] RSPM (R 4.0.0)
## HGNChelper * 0.8.1 2019-10-24 [1] RSPM (R 4.0.0)
## Hmisc 4.4-0 2020-03-23 [1] RSPM (R 4.0.0)
## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0)
## htmlTable 1.13.3 2019-12-04 [1] RSPM (R 4.0.0)
## htmltools 0.4.0 2019-10-04 [1] RSPM (R 4.0.0)
## htmlwidgets 1.5.1 2019-10-08 [1] RSPM (R 4.0.0)
## httpuv 1.5.4 2020-06-06 [1] RSPM (R 4.0.0)
## httr 1.4.1 2019-08-05 [1] RSPM (R 4.0.0)
## igraph 1.2.5 2020-03-19 [1] RSPM (R 4.0.0)
## import 1.1.0 2015-06-22 [1] RSPM (R 4.0.0)
## impute 1.62.0 2020-04-27 [1] Bioconductor
## infer * 0.5.1 2019-11-19 [1] RSPM (R 4.0.0)
## inline 0.3.15 2018-05-18 [1] RSPM (R 4.0.0)
## ipred 0.9-9 2019-04-28 [1] RSPM (R 4.0.0)
## IRanges * 2.22.2 2020-05-21 [1] Bioconductor
## iterators 1.0.12 2019-07-26 [1] RSPM (R 4.0.0)
## janeaustenr 0.1.5 2017-06-10 [1] RSPM (R 4.0.0)
## janitor * 2.0.1 2020-04-12 [1] RSPM (R 4.0.0)
## jpeg 0.1-8.1 2019-10-24 [1] RSPM (R 4.0.0)
## jsonlite 1.6.1 2020-02-02 [1] RSPM (R 4.0.0)
## kableExtra * 1.1.0 2019-03-16 [1] RSPM (R 4.0.0)
## knitr * 1.28 2020-02-06 [1] RSPM (R 4.0.0)
## labeling 0.3 2014-08-23 [1] RSPM (R 4.0.0)
## later 1.1.0.1 2020-06-05 [1] RSPM (R 4.0.0)
## lattice * 0.20-41 2020-04-02 [2] CRAN (R 4.0.0)
## latticeExtra 0.6-29 2019-12-19 [1] RSPM (R 4.0.0)
## lava 1.6.7 2020-03-05 [1] RSPM (R 4.0.0)
## lazyeval 0.2.2 2019-03-15 [1] RSPM (R 4.0.0)
## lhs 1.0.2 2020-04-13 [1] RSPM (R 4.0.0)
## lifecycle 0.2.0 2020-03-06 [1] RSPM (R 4.0.0)
## limma 3.44.3 2020-06-12 [1] Bioconductor
## listenv 0.8.0 2019-12-05 [1] RSPM (R 4.0.0)
## lme4 1.1-23 2020-04-07 [1] RSPM (R 4.0.0)
## locfit 1.5-9.4 2020-03-25 [1] RSPM (R 4.0.0)
## loo 2.2.0 2019-12-19 [1] RSPM (R 4.0.0)
## lubridate 1.7.8 2020-04-06 [1] RSPM (R 4.0.0)
## magrittr 1.5 2014-11-22 [1] RSPM (R 4.0.0)
## markdown 1.1 2019-08-07 [1] RSPM (R 4.0.0)
## MASS 7.3-51.6 2020-04-26 [2] RSPM (R 4.0.0)
## Matrix * 1.2-18 2019-11-27 [2] CRAN (R 4.0.0)
## matrixStats * 0.56.0 2020-03-13 [1] RSPM (R 4.0.0)
## memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.0)
## mgcv * 1.8-31 2019-11-09 [2] CRAN (R 4.0.0)
## mime 0.9 2020-02-04 [1] RSPM (R 4.0.0)
## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.0.0)
## minqa 1.2.4 2014-10-09 [1] RSPM (R 4.0.0)
## ModelMetrics 1.2.2.2 2020-03-17 [1] RSPM (R 4.0.0)
## modelr 0.1.8 2020-05-19 [1] RSPM (R 4.0.0)
## moduleScoreR * 0.0.0.9200 2020-09-18 [1] Github (milescsmith/moduleScoreR@e6db70f)
## munsell 0.5.0 2018-06-12 [1] RSPM (R 4.0.0)
## nlme * 3.1-148 2020-05-24 [2] RSPM (R 4.0.0)
## nloptr 1.2.2.1 2020-03-11 [1] RSPM (R 4.0.0)
## NMF 0.22.0 2020-02-12 [1] RSPM (R 4.0.0)
## nnet 7.3-14 2020-04-26 [2] RSPM (R 4.0.0)
## officer 0.3.11 2020-05-18 [1] RSPM (R 4.0.0)
## openxlsx 4.1.5 2020-05-06 [1] RSPM (R 4.0.0)
## paletteer * 1.2.0 2020-06-07 [1] RSPM (R 4.0.0)
## parsnip * 0.1.1 2020-05-06 [1] RSPM (R 4.0.0)
## pheatmap * 1.0.12 2019-01-04 [1] RSPM (R 4.0.0)
## pillar 1.4.4 2020-05-05 [1] RSPM (R 4.0.0)
## pkgbuild 1.0.8 2020-05-07 [1] RSPM (R 4.0.0)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.0)
## pkgmaker 0.31.1 2020-03-19 [1] RSPM (R 4.0.0)
## plotly * 4.9.2.1 2020-04-04 [1] RSPM (R 4.0.0)
## plyr 1.8.6 2020-03-03 [1] RSPM (R 4.0.0)
## png 0.1-7 2013-12-03 [1] RSPM (R 4.0.0)
## polyclip 1.10-0 2019-03-14 [1] RSPM (R 4.0.0)
## preprocessCore 1.50.0 2020-04-27 [1] Bioconductor
## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.0)
## prismatic 0.2.0 2019-12-01 [1] RSPM (R 4.0.0)
## pROC 1.16.2 2020-03-19 [1] RSPM (R 4.0.0)
## processx 3.4.2 2020-02-09 [1] RSPM (R 4.0.0)
## prodlim 2019.11.13 2019-11-17 [1] RSPM (R 4.0.0)
## progress 1.2.2 2019-05-16 [1] RSPM (R 4.0.0)
## promises 1.1.0 2019-10-04 [1] RSPM (R 4.0.0)
## ps 1.3.3 2020-05-08 [1] RSPM (R 4.0.0)
## purrr * 0.3.4 2020-04-17 [1] RSPM (R 4.0.0)
## qvalue 2.20.0 2020-04-27 [1] Bioconductor
## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0)
## randomForest * 4.6-14 2018-03-25 [1] RSPM (R 4.0.0)
## RColorBrewer * 1.1-2 2014-12-07 [1] RSPM (R 4.0.0)
## Rcpp 1.0.4.6 2020-04-09 [1] RSPM (R 4.0.0)
## RCurl 1.98-1.2 2020-04-18 [1] RSPM (R 4.0.0)
## readr * 1.3.1 2018-12-21 [1] RSPM (R 4.0.0)
## readxl * 1.3.1 2019-03-13 [1] RSPM (R 4.0.0)
## recipes * 0.1.12 2020-05-01 [1] RSPM (R 4.0.0)
## registry 0.5-1 2019-03-05 [1] RSPM (R 4.0.0)
## rematch2 2.1.2 2020-05-01 [1] RSPM (R 4.0.0)
## reprex 0.3.0 2019-05-16 [1] RSPM (R 4.0.0)
## reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.0.0)
## rio 0.5.16 2018-11-26 [1] RSPM (R 4.0.0)
## rlang * 0.4.6 2020-05-02 [1] RSPM (R 4.0.0)
## rmarkdown 2.2 2020-05-31 [1] RSPM (R 4.0.0)
## rngtools 1.5 2020-01-23 [1] RSPM (R 4.0.0)
## rpart 4.1-15 2019-04-12 [2] CRAN (R 4.0.0)
## rsample * 0.0.7 2020-06-04 [1] RSPM (R 4.0.0)
## rsconnect 0.8.16 2019-12-13 [1] RSPM (R 4.0.0)
## RSQLite 2.2.0 2020-01-07 [1] RSPM (R 4.0.0)
## rstan 2.19.3 2020-02-11 [1] RSPM (R 4.0.0)
## rstanarm 2.19.3 2020-02-11 [1] RSPM (R 4.0.0)
## rstantools 2.1.0 2020-06-01 [1] RSPM (R 4.0.0)
## rstatix * 0.6.0.999 2020-09-18 [1] Github (milescsmith/rstatix@6f5e0a6)
## rstudioapi 0.11 2020-02-07 [1] RSPM (R 4.0.0)
## rsvd 1.0.3 2020-02-17 [1] RSPM (R 4.0.0)
## rvcheck 0.1.8 2020-03-01 [1] RSPM (R 4.0.0)
## rvest 0.3.5 2019-11-08 [1] RSPM (R 4.0.0)
## S4Vectors * 0.26.1 2020-05-16 [1] Bioconductor
## scales * 1.1.1 2020-05-11 [1] RSPM (R 4.0.0)
## scatterpie 0.1.4 2019-11-08 [1] RSPM (R 4.0.0)
## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.0)
## shiny 1.4.0.2 2020-03-13 [1] RSPM (R 4.0.0)
## shinyjs 1.1 2020-01-13 [1] RSPM (R 4.0.0)
## shinystan 2.5.0 2018-05-01 [1] RSPM (R 4.0.0)
## shinythemes 1.1.2 2018-11-06 [1] RSPM (R 4.0.0)
## snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.0.0)
## snow 0.4-3 2018-09-14 [1] RSPM (R 4.0.0)
## SnowballC 0.7.0 2020-04-01 [1] RSPM (R 4.0.0)
## StanHeaders 2.21.0-3 2020-05-28 [1] RSPM (R 4.0.0)
## statmod 1.4.34 2020-02-17 [1] RSPM (R 4.0.0)
## storr 1.2.1 2018-10-18 [1] RSPM (R 4.0.0)
## stringi 1.4.6 2020-02-17 [1] RSPM (R 4.0.0)
## stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.0.0)
## SummarizedExperiment * 1.18.2 2020-07-09 [1] Bioconductor
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## threejs 0.3.3 2020-01-21 [1] RSPM (R 4.0.0)
## tibble * 3.0.1 2020-04-20 [1] RSPM (R 4.0.0)
## tidygraph 1.2.0 2020-05-12 [1] RSPM (R 4.0.0)
## tidymodels * 0.1.0 2020-02-16 [1] RSPM (R 4.0.0)
## tidyposterior 0.0.2 2018-11-15 [1] RSPM (R 4.0.0)
## tidypredict 0.4.5 2020-02-10 [1] RSPM (R 4.0.0)
## tidyr * 1.1.0 2020-05-20 [1] RSPM (R 4.0.0)
## tidyselect * 1.1.0 2020-05-11 [1] RSPM (R 4.0.0)
## tidytext 0.2.4 2020-04-17 [1] RSPM (R 4.0.0)
## tidyverse * 1.3.0 2019-11-21 [1] RSPM (R 4.0.0)
## timeDate 3043.102 2018-02-21 [1] RSPM (R 4.0.0)
## tokenizers 0.2.1 2018-03-29 [1] RSPM (R 4.0.0)
## triebeard 0.3.0 2016-08-04 [1] RSPM (R 4.0.0)
## tune * 0.1.0 2020-04-02 [1] RSPM (R 4.0.0)
## tweenr 1.0.1 2018-12-14 [1] RSPM (R 4.0.0)
## tximport * 1.16.1 2020-06-05 [1] Bioconductor
## txtq 0.2.0 2019-10-15 [1] RSPM (R 4.0.0)
## urltools 1.7.3 2019-04-14 [1] RSPM (R 4.0.0)
## uuid 0.1-4 2020-02-26 [1] RSPM (R 4.0.0)
## uwot * 0.1.8 2020-03-16 [1] RSPM (R 4.0.0)
## vctrs 0.3.1 2020-06-05 [1] RSPM (R 4.0.0)
## viridis * 0.5.1 2018-03-29 [1] RSPM (R 4.0.0)
## viridisLite * 0.3.0 2018-02-01 [1] RSPM (R 4.0.0)
## webshot 0.5.2 2019-11-22 [1] RSPM (R 4.0.0)
## WGCNA * 1.69 2020-02-28 [1] RSPM (R 4.0.0)
## withr 2.2.0 2020-04-20 [1] RSPM (R 4.0.0)
## workflows * 0.1.1 2020-03-17 [1] RSPM (R 4.0.0)
## xfun 0.14 2020-05-20 [1] RSPM (R 4.0.0)
## XML 3.99-0.3 2020-01-20 [1] RSPM (R 4.0.0)
## xml2 1.3.2 2020-04-23 [1] RSPM (R 4.0.0)
## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.0.0)
## xts 0.12-0 2020-01-19 [1] RSPM (R 4.0.0)
## XVector 0.28.0 2020-04-27 [1] Bioconductor
## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.0)
## yardstick * 0.0.6 2020-03-17 [1] RSPM (R 4.0.0)
## zip 2.0.4 2019-09-01 [1] RSPM (R 4.0.0)
## zlibbioc 1.34.0 2020-04-27 [1] Bioconductor
## zoo 1.8-8 2020-05-02 [1] RSPM (R 4.0.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library